Radiative Transfer Models are fundamental tools in Earth observation and climate research. However, their high computational demands often limit their application in real-time or large-scale scenarios — especially as the volume and complexity of hyperspectral satellite data continue to increase.

Common alternatives, such as look-up tables (LUTs), can reduce computational load but are memory-intensive and lack flexibility. Machine learning-based emulators offer a promising alternative by producing fast and accurate approximations of RTM outputs.

AMLEC aims to support the development of statistical models that emulate RTM behaviour, enabling faster atmospheric data processing, improved climate simulations, and enhanced remote sensing workflows. The challenge also addresses the difficulties posed by high-dimensional inputs and the complex physics embedded in RTMs.

Participants will have access to:

• Training and test datasets based on atmospheric RTMs

• Evaluation metrics and submission guidelines

• All materials are hosted on Hugging Face: RTM Emulation Dataset

All scripts will be made publicly available after the end of the challenge.

• A peer-reviewed publication summarising the challenge and profiling the top three performing models will be submitted to a remote sensing journal.

• Results will also be presented at the ECML-PKDD conference, providing visibility within the research community.

• Participants will gain valuable experience in atmospheric modelling and machine learning-based emulation techniques.